{"title":"干涉图对平面和柱面参考光束波前像差灵敏度的比较分析","authors":"P. A. Khorin, A. P. Dzyuba, N. V. Petrov","doi":"10.3103/S1060992X23050090","DOIUrl":null,"url":null,"abstract":"<p>The paper investigates the sensitivity of interferograms formed using the structured reference beams. The parameters of the reference beam are selected to improve the visualization of aberrations in the interferograms. A study carried out on the use of reference beams with cylindrical wavefronts in the interferograms formation to improve the aberrations recognition using a convolutional neural network. The applying of a cylindrical reference beam instead of a plane one in the interference method for recognition of wave aberrations based on neural networks with Xception architecture makes it possible to reduce the mean absolute error by more than 30%. In this work, for each type of interferogram, the model was trained for 80 epochs, which took about 1.8 hours using GeForce RTX 2070 graphics card. However, after completing this training once, we obtain a model that allows us to make forecasts in 0.055 s for every new interferogram of the same type.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative Analysis of the Interferogram Sensitivity to Wavefront Aberrations Recorded with Plane and Cylindrical Reference Beams\",\"authors\":\"P. A. Khorin, A. P. Dzyuba, N. V. Petrov\",\"doi\":\"10.3103/S1060992X23050090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The paper investigates the sensitivity of interferograms formed using the structured reference beams. The parameters of the reference beam are selected to improve the visualization of aberrations in the interferograms. A study carried out on the use of reference beams with cylindrical wavefronts in the interferograms formation to improve the aberrations recognition using a convolutional neural network. The applying of a cylindrical reference beam instead of a plane one in the interference method for recognition of wave aberrations based on neural networks with Xception architecture makes it possible to reduce the mean absolute error by more than 30%. In this work, for each type of interferogram, the model was trained for 80 epochs, which took about 1.8 hours using GeForce RTX 2070 graphics card. However, after completing this training once, we obtain a model that allows us to make forecasts in 0.055 s for every new interferogram of the same type.</p>\",\"PeriodicalId\":721,\"journal\":{\"name\":\"Optical Memory and Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optical Memory and Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.3103/S1060992X23050090\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Memory and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.3103/S1060992X23050090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
Comparative Analysis of the Interferogram Sensitivity to Wavefront Aberrations Recorded with Plane and Cylindrical Reference Beams
The paper investigates the sensitivity of interferograms formed using the structured reference beams. The parameters of the reference beam are selected to improve the visualization of aberrations in the interferograms. A study carried out on the use of reference beams with cylindrical wavefronts in the interferograms formation to improve the aberrations recognition using a convolutional neural network. The applying of a cylindrical reference beam instead of a plane one in the interference method for recognition of wave aberrations based on neural networks with Xception architecture makes it possible to reduce the mean absolute error by more than 30%. In this work, for each type of interferogram, the model was trained for 80 epochs, which took about 1.8 hours using GeForce RTX 2070 graphics card. However, after completing this training once, we obtain a model that allows us to make forecasts in 0.055 s for every new interferogram of the same type.
期刊介绍:
The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.